133 citations · 223 across the 10 of their papers we have counts for
10 papers
Large Language Models in K-12 Education: Alignment with State Curriculum Standards and Student Personas
Lisa Korver, Tomo Lazovich, Sherief Reda
As Large Language Models (LLMs) become increasingly popular in educational settings, they raise important questions about the ethical implications of their use. Publicly available…
Reducing Population-level Inequality Can Improve Demographic Group Fairness: a Twitter Case Study
Avijit Ghosh, Tomo Lazovich, Kristian Lum +1
Many existing fairness metrics measure group-wise demographic disparities in system behavior or model performance. Calculating these metrics requires access to demographic informat…
Filter bubbles and affective polarization in user-personalized large language model outputs
Tomo Lazovich
Echoing the history of search engines and social media content rankings, the advent of large language models (LLMs) has led to a push for increased personalization of model outputs…
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
Vanessa Cai, Pradeep Prabakar, Manuel Serrano Rebuelta +8
Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are spl…
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Amanda Bower, Kristian Lum, Tomo Lazovich +2
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic o…
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
Tomo Lazovich, Luca Belli, Aaron Gonzales +5
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal b…
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Rebecca L. Russell, Louis Kim, Lei H. Hamilton +5
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can…
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Jacob Harer, Onur Ozdemir, Tomo Lazovich +4
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target do…
Automated software vulnerability detection with machine learning
Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell +13
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered i…
Backgrounds in H->WW*->lvlv with ATLAS
Tomo Lazovich
We present techniques used to estimate the backgrounds in the search for the Standard Model Higgs boson in the H->WW*->lvlv decay channel with the ATLAS experiment at the LHC. The…